What isLearning Science?

What isLearning Science?

Learning science is the study of how people acquire knowledge and skills. It utilizes data from a variety of scientific disciplines, including cognitive science, motivational psychology, behavioral economics, neuroscience, and more to get insights into how people learn (and how they do not learn). By analyzing this data, we’re able to more clearly identify student outcomes and create curricula and learning environments with a higher likelihood of positively impacting students.

Learning engineering is where learning science comes to life. It is the application of learning science into specific contexts, including constraints such as regulatory, legal, technology, and time constraints. The goal of learning engineering is to create an effective learning environment that serves the needs of the learner and fits the constraints. Learning science provides the hypotheses and evidence that learning engineering uses to build real-world learning environments that work better for students.

I’m Bror Saxberg. I’m the Chief Learning Officer of Kaplan, and so I look across all of Kaplan’s businesses around the globe. And what I really do is think about how can we apply Learning Science and better learning measurement techniques to improve the learning outcomes for all our students, the million plus students we have around the globe.

So Learning Science is what it sounds like, the science of how people learn. It actually draws from lots of different disciplines. It draws from cognitive science, it draws from motivational psychology, from behavioral economics, even a little bit from neuroscience. A whole range of different domains have valuable insights about how people learn and don’t learn that can be drawn on when you’re trying to make better learning environments and trying to figure out, “Well, what are the key decisions and tasks that you’re trying to target?”

Learning Engineering is actually the hard work of using the science of learning and applying it in specific contexts. Regulatory constraints, economic constraints, time constraints, all those things to actually wind up with a high-quality, effective learning environment that satisfies the needs of learners, as well as regulators and other stakeholders in the whole thing. So Learning Science gives you evidence and data that you can draw from. Learning Engineering is the really creative task of using the science to actually build real-world environments that work better for students.

Now, there’s new skills happening all the time because of technology changes going in that create some jobs that are about these technologies. But even more so, professionals of all kinds and levels are having to work alongside various different technologies and information devices to do their work. So how does Learning Science help with this? Well, it helps on a couple of dimensions. One dimension is what Learning Science says about expertise. The key thing about experts is, they end up, after 10 years or so of deliberate practice, with a lot of principles, and processes, and tasks, and decision making that has become kind of burned into long-term memory. And that’s great because then it’s automated, it can happen very quickly. But it’s a problem for creating new learning environments because that kind of automated capability is no longer verbally accessible.

So one of the things we can do is make sure we’re identifying experts. And new experts are gonna keep popping up every few years in fields as the fields change and keep linking what those new experts do to the new learning environments that we create. The other way Learning Science can really help us with these new skills and how to master them efficiently is to take advantage of what Learning Science says about which types of practice and feedback, for example, are best for what kinds of learning outcomes.

It turns out there’s a lot of research over the last several decades about this that have not yet been put to work. So there are real opportunities to make better learning environments that alter the kinds of practice and feedback that students actually use. Now, you notice I haven’t said anything about technology yet. Well, in the course of making these better learning environments and having better ways for minds to engage in practice and feedback, we can use technology to make those better activities become more affordable, more reliable, more data rich, more available.

When you think about what’s happening with automation around the globe, you think about how skills, and information, and tasks are now being moved across borders completely differently than they used to be. It’s really clear that the nature of jobs is changing a lot. You’ll see all around the globe, people are beginning to think more and more about their own kinds of jobs. The gig economy, things like Uber and contract work of all different kinds, are becoming increasingly common for both basic level work like the driving situation, but also for very complex cognitive work in terms of coding, design work, and things like that.

What this means is that instead of us trusting other institutions like the university, or the school, or the corporation, to give us what we need to know, we all have to take more responsibility to think through, “What is it we need for our own mix of skills?” The other thing that’s happening that’s profound is we are living longer and longer. So if we’re living to 90 years old and careers are changing faster and faster, this means we all have to change our careers over our lifetimes as well. We cannot afford to have lots of 40-year-olds and 50-year-olds with nothing to do.

So that means we all need to figure out, “Where do we go to get our skills? What are those high-value skills? How do they change, and how do we use some of the new automation, the new computers, the new techniques, to actually give ourselves new skills for the long haul?” And that’s what I do here at Kaplan is talk about those things with my colleagues all around the firm.

As skillset requirements evolve with how we use technology in the workplace, we need to change what our employees decide and do…faster and faster. To ensure we can make these changes, it is critical that we use learning science to understand how our instructional processes also need to evolve.

Your Experts Are Doers, Not Teachers

Historically, we’ve relied on experts to be teachers. While that’s been done with the best of intentions, it’s ultimately not fair to the learner or the expert we’ve tapped to teach. That’s because over time, experts develop processes and patterns for performing certain tasks that are burned into long-term memory. This makes them incredibly efficient at executing certain (potentially quite complex) processes, but also makes them less likely to be able to verbalize the steps and tasks they’ve mentally automated over time. Techniques drawn from learning science can help your organization identify this non-verbal automated knowledge that experts have, so you can incorporate it into the training for a specific task.

Aligning Feedback and Practice With Desired Learning Outcomes

Another way you can apply learning science into the training of your employees is to better understand the feedback and practice that work best for propelling your employees to the desired learning outcomes. There are decades of research that have not yet been put to practical use that provide us with real opportunities to create learning environments that engage learners in the types of activities proven to drive a specific type of learning outcome.

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Learning Science Should Come Before Technology

That old phrase about putting the cart before the horse rings true in education, and particularly in organizational instruction and training. We’ve become obsessed with buzzwords like adaptive, personalized, and AI, without really understanding how to use these technological advancements to actually improve the employee’s ability to learn.

Learning science, or even learning engineering, is not the same as educational technology. Rather, results from learning science should inform the way we utilize technology in the educational process, so that it lines up with how learning actually occurs. Technology, when used to execute a plan informed by learning science, can be incredibly effective at creating more affordable, reliable, accessible, data-rich, and personalized learning opportunities. Without the consideration of learning science, technology is just a new way to do the same thing (good or bad) we’ve always done.

Learning Science Can Help Meet the Changing Needs of Your Business

Take a look at the required skillset for a position on your staff. Now think back 20, or even 10, years ago. Chances are, quite a bit has changed. In some cases, that position may not have even existed 10 years ago. As business needs have evolved, the skillsets employees need have evolved as well, and they will continue to evolve in the future at an even faster rate.

As a result, the things your employees learned in college 5, 10, or 20 years ago might be largely irrelevant in your modern-day workplace. So your success relies upon understanding what it takes to become proficient in the skillsets that matter for your company (learning science) and develop learning environments that efficiently enable employees to acquire them (learning engineering).